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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: xls-r-uzbek-cv8 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xls-r-uzbek-cv8 |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3066 |
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- Wer: 0.3855 |
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- Cer: 0.0778 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 100.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| 3.1401 | 3.25 | 500 | 3.1146 | 1.0 | 1.0 | |
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| 2.7484 | 6.49 | 1000 | 2.2842 | 1.0065 | 0.7069 | |
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| 1.0899 | 9.74 | 1500 | 0.5414 | 0.6125 | 0.1351 | |
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| 0.9465 | 12.99 | 2000 | 0.4566 | 0.5635 | 0.1223 | |
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| 0.8771 | 16.23 | 2500 | 0.4212 | 0.5366 | 0.1161 | |
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| 0.8346 | 19.48 | 3000 | 0.3994 | 0.5144 | 0.1102 | |
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| 0.8127 | 22.73 | 3500 | 0.3819 | 0.4944 | 0.1051 | |
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| 0.7833 | 25.97 | 4000 | 0.3705 | 0.4798 | 0.1011 | |
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| 0.7603 | 29.22 | 4500 | 0.3661 | 0.4704 | 0.0992 | |
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| 0.7424 | 32.47 | 5000 | 0.3529 | 0.4577 | 0.0957 | |
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| 0.7251 | 35.71 | 5500 | 0.3410 | 0.4473 | 0.0928 | |
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| 0.7106 | 38.96 | 6000 | 0.3401 | 0.4428 | 0.0919 | |
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| 0.7027 | 42.21 | 6500 | 0.3355 | 0.4353 | 0.0905 | |
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| 0.6927 | 45.45 | 7000 | 0.3308 | 0.4296 | 0.0885 | |
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| 0.6828 | 48.7 | 7500 | 0.3246 | 0.4204 | 0.0863 | |
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| 0.6706 | 51.95 | 8000 | 0.3250 | 0.4233 | 0.0868 | |
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| 0.6629 | 55.19 | 8500 | 0.3264 | 0.4159 | 0.0849 | |
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| 0.6556 | 58.44 | 9000 | 0.3213 | 0.4100 | 0.0835 | |
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| 0.6484 | 61.69 | 9500 | 0.3182 | 0.4124 | 0.0837 | |
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| 0.6407 | 64.93 | 10000 | 0.3171 | 0.4050 | 0.0825 | |
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| 0.6375 | 68.18 | 10500 | 0.3150 | 0.4039 | 0.0822 | |
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| 0.6363 | 71.43 | 11000 | 0.3129 | 0.3991 | 0.0810 | |
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| 0.6307 | 74.67 | 11500 | 0.3114 | 0.3986 | 0.0807 | |
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| 0.6232 | 77.92 | 12000 | 0.3103 | 0.3895 | 0.0790 | |
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| 0.6216 | 81.17 | 12500 | 0.3086 | 0.3891 | 0.0790 | |
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| 0.6174 | 84.41 | 13000 | 0.3082 | 0.3881 | 0.0785 | |
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| 0.6196 | 87.66 | 13500 | 0.3059 | 0.3875 | 0.0782 | |
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| 0.6174 | 90.91 | 14000 | 0.3084 | 0.3862 | 0.0780 | |
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| 0.6169 | 94.16 | 14500 | 0.3070 | 0.3860 | 0.0779 | |
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| 0.6166 | 97.4 | 15000 | 0.3066 | 0.3855 | 0.0778 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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